Transfer Learning in the Transformer Model for Thermal Comfort Prediction: A Case of Limited Data

Abstract

The HVAC (Heating, Ventilation, and Air Conditioning) system is an important component of a building’s energy consumption, and its primary function is to provide a comfortable thermal environment for occupants. Accurate prediction of occupant thermal comfort is essential for improving building energy utilization as well as health and work efficiency. Therefore, the development of accurate thermal comfort prediction models is of great value. Deep learning based on data-driven techniques has excellent potential for predicting thermal comfort due to the development of artificial intelligence. However, the inability to obtain large quantities of detailed thermal comfort labeling data from residents presents a substantial challenge to the modeling endeavor. This paper proposes a building-to-building transfer learning framework to make deep learning models applicable in data-limited interior building environments, thereby resolving the issue and enhancing model predictive performance. The transfer learning method (TL) is applied to a novel technology dubbed the Transformer model, which has demonstrated outstanding performance in data trend prediction. The model exploits the spatiotemporal relationship of data regarding thermal comfort. Experiments are conducted using the source dataset (Scales project dataset and ASHRAE RP-884 dataset) and the target dataset (Medium US office dataset), and the results show that the proposed TL-Transformer achieves 62.6% accuracy, 57% precision, and a 59% F1 score, and the prediction performance is better than other existing methods. The model is useful for predicting indoor thermal comfort in buildings with limited data, and its validity is verified by experimental results.

Privacy preserved and decentralized thermal comfort prediction model for smart buildings using federated learning

“…The study compares with the existing study ( Zhang & Li, 2023 ) as shown in Table 3 . According to Zhang & Li (2023) , the accuracy is 62.6% accuracy, 57.0% precision, and 59.0% F-score, whereas the proposed method obtained 85.0% accuracy, 86.0% precision, 90.0% recall and 88.0% F-score.…”

“…The study compares with the existing study ( Zhang & Li, 2023 ) as shown in Table 3 . According to Zhang & Li (2023) , the accuracy is 62.6% accuracy, 57.0% precision, and 59.0% F-score, whereas the proposed method obtained 85.0% accuracy, 86.0% precision, 90.0% recall and 88.0% F-score. The proposed technique performs better in this context than the Base Paper, indicating that our technique outperforms in thermal comfort model prediction for smart buildings.…”

Enhanced Thermal Comfort Prediction Model by Addressing Outliers and Data Imbalance

“…The results demonstrated that the ensemble TL approach enhanced the accuracy of thermal comfort predictions for two target subjects using a model pre-trained on a source dataset. In 2023, Zhang and Li [42] proposed integrating transfer learning with a transformer model to predict thermal comfort, utilizing the ASHRAE RP-884 dataset from the Scales project as the source and the Medium US dataset as the target domain. The proposed TL-Transformer model achieved an accuracy of 62.6%, outperforming other state-of-the-art methods tested in their experiments.…”